Recommending and initiating contactless events based on machine learning

Abstract

Aspects of the disclosure relate to use of supervised, unsupervised, semi-supervised, or reinforcement-based machine learning algorithm(s) to perform pattern recognition and/or cluster detection on historical transactional data in order to predict events that may occur in the future and are candidates for automated delivery of items from a source to a customer in a contactless manner, via a secure delivery device, to minimize health risk(s) to the customer. The items may be securely delivered in an automated manner such as, for example, by use of a drone or autonomous vehicle, which may have advanced sensors to facilitate various aspects of the delivery. The items may be contained in a secure carrier coupled to the delivery device and may be unlocked locally or remotely using mechanical or digital means. Novel logical systems, architectures, machines, platforms, delivery devices and components thereof, and methods are disclosed.

What is claimed is: 
     
       1. A predictive automated contactless delivery method for a computing machine to securely deliver an item by a drone from a source to a customer in a contactless manner, said customer having a customer communication device, said drone communicatively coupled to said computing machine, said method comprising the steps of:
 loading, by the computing machine, contactless delivery training data, an automated contactless delivery output format, and historical data identifying a plurality of historical transactions for the customer; 
 training, by computing machine, a machine learning algorithm based on the contactless delivery training data, the automated contactless delivery output format, and the historical data identifying the plurality of historical transactions for the customer, said machine learning algorithm configured to utilize semi-supervised machine learning; 
 identifying, by the machine learning algorithm, a recurring transaction; 
 communicating, by the computing machine to the customer communication device, the recurring transaction; 
 receiving, by the computing machine from the communication device, a positive customer preference notification that the customer would like the recurring transaction to be a future recurring automated contactless transaction; 
 storing, by the computing machine based on the customer preference notification, a schedule for securely delivering to the customer the item corresponding to the future recurring automated contactless transaction; 
 instructing, by the computing machine based on the schedule, the drone to execute the future recurring automated contactless transaction to securely deliver the item from the source to the customer, said item securely stored in a locked secure carrier removably coupled to the drone; 
 detecting, by at least one anti-tampering security sensor on the drone, any tampering of the locked secure carrier; 
 transmitting, by the drone to the computing machine, tampering information if any said tampering of the locked secure carrier is detected; 
 detecting, by the drone, GPS location information during flight of the drone to the customer; 
 transmitting, by the drone to the computing machine, the GPS location information; 
 monitoring, by the computing machine, the GPS location received from the drone; 
 transmitting, by the computing machine to the customer communication device, the GPS location information during said flight of the drone to the customer; 
 detecting, by the drone, flight security information based on a flight security sensor; 
 transmitting, by the drone to the computing machine, the flight security information; 
 monitoring, by the computing machine, the flight security information; 
 unlocking, the locked secure carrier after delivery to the customer so that the customer can remove the item from the locked secure carrier; 
 capturing, by the drone, visual proof of said delivery; 
 transmitting, by the drone to the computing machine, the visual proof of said delivery; and 
 transmitting, by the computing machine to the customer communication device, a delivery notification regarding a delivery status for the item and the visual proof of said delivery, 
 whereby the one or more items are delivered to the customer without any human contact in order to minimize health risks to the customer. 
 
     
     
       2. The predictive automated contactless delivery method of  claim 1  wherein the semi-supervised machine learning is configured to perform pattern recognition on the historical data. 
     
     
       3. The predictive automated contactless delivery method of  claim 1  wherein the semi-supervised machine learning is configured to detect clusters in the historical data. 
     
     
       4. The predictive automated contactless delivery method of  claim 2  wherein the semi-supervised machine learning is configured to detect clusters in the historical data. 
     
     
       5. Non-transitory computer-readable media containing predictive automated contactless delivery processor-executable instructions for a computing machine to securely deliver an item by a drone from a source to a customer in a contactless manner, said customer having a customer communication device, said drone communicatively coupled to said computing machine, said computer-readable media comprising:
 loading instructions for the computing machine to load contactless delivery training data, an automated contactless delivery output format, and historical data identifying a plurality of historical transactions for the customer into memory; 
 training instructions for the computing machine to train a machine learning algorithm based on the contactless delivery training data, the automated contactless delivery output format, and the historical data identifying the plurality of historical transactions for the customer; 
 identification instructions for the machine learning algorithm to identify a recurring transaction; 
 communication instructions for the computing machine to communicate the recurring transaction to the customer communication device, 
 reception instructions for the computing machine to receive from the communication device a customer preference notification as to whether the customer would like the recurring transaction to be a future recurring automated contactless transaction; 
 instructions to execute when the customer preference notification received is positive comprising:
 scheduling instructions for the computing machine to schedule securely delivery to the customer the item corresponding to the future recurring automated contactless transaction; 
 flight instructions for the drone to execute the future recurring automated contactless transaction to securely deliver the item from the source to the customer based on the schedule, said item securely stored in a locked secure carrier removably coupled to the drone; 
 detection instructions to detect, by at least one anti-tampering security sensor on the drone, any tampering of the locked secure carrier; 
 transmission instructions to transmit by the drone to the computing machine tampering information if any said tampering is detected; 
 geolocating instructions for the drone to collect GPS location information during flight of the drone to the customer; 
 transmission instructions for the drone to transmit to the computing machine the GPS location information; 
 monitoring instructions for the computing machine to monitor the GPS location received from the drone; 
 transmission instructions for the computing machine to transmit to the customer communication device, the GPS location information during said flight of the drone to the customer; 
 detection instructions for the drone to detect flight security information based on a flight security sensor; 
 transmission instructions for the drone to transmit to the computing machine the flight security information; 
 monitoring instructions for the computing machine to monitor the flight security information; 
 unlocking instructions to unlock the locked secure carrier after delivery to the customer so that the customer can remove the item from the locked secure carrier; 
 capturing instructions by the drone to record visual proof of said delivery; 
 transmission instructions for the drone to transmit to the computing machine the visual proof of said delivery; and 
 transmission instructions for the computing machine to transmit to the customer communication device, a delivery notification regarding a delivery status for the item and the visual proof of said delivery, 
 
 whereby the one or more items are delivered to the customer without any human contact in order to minimize health risks to the customer. 
 
     
     
       6. The non-transitory computer-readable media of  claim 5  wherein the machine learning algorithm is configured to utilize supervised machine learning. 
     
     
       7. The non-transitory computer-readable media of  claim 6  wherein the supervised machine learning is configured to perform pattern recognition on the historical data. 
     
     
       8. The non-transitory computer-readable media of  claim 6  wherein the supervised machine learning is configured to detect clusters in the historical data. 
     
     
       9. The non-transitory computer-readable media of  claim 7  wherein the supervised machine learning is configured to detect clusters in the historical data. 
     
     
       10. The non-transitory computer-readable media of  claim 5  wherein the machine learning algorithm is configured to utilize semi-supervised machine learning. 
     
     
       11. The non-transitory computer-readable media of  claim 10  wherein the semi-supervised machine learning is configured to perform pattern recognition on the historical data. 
     
     
       12. The non-transitory computer-readable media of  claim 10  wherein the semi-supervised machine learning is configured to detect clusters in the historical data. 
     
     
       13. The non-transitory computer-readable media of  claim 11  wherein the semi-supervised machine learning is configured to detect clusters in the historical data.